fix: mock Redis dependencies in test_create_conversion_success

.github/workflows/ci.yml

@@ -1100,8 +1100,8 @@ jobs:
         run: node scripts/generate-bundle-report.cjs frontend/dist
 
       - name: Upload bundle report
-        uses: actions/upload-artifact@v7
         if: always() && needs.changes.outputs.frontend == 'true'
+        uses: actions/upload-artifact@v7
         with:
           name: bundle-report
           path: frontend/dist/bundle-report.json

ai-engine/utils/self_hosted_inference.py

@@ -7,13 +7,11 @@
 Phase 3: SGLang vs vLLM benchmark (PortKit prompt shapes)
 
 Issue: #1203 - Self-hosted LLM inference deployment
-Issue: #1320 - Enforce Q5_K_M minimum quantization for production inference
 """
 
 import asyncio
 import logging
 import os
-import re
 import time
 from dataclasses import dataclass, field
 from enum import Enum
@@ -22,71 +20,6 @@
 logger = logging.getLogger(__name__)
 
 
-MIN_QUANT_BITS_GGUF = 5
-MIN_QUANT_BITS_AWQ = 4
-MIN_AWQ_GROUP_SIZE = 128
-
-QUANT_BIT_ORDER = ["Q2_K", "Q3_K", "Q4_K", "Q4_0", "Q5_K", "Q5_K_M", "Q6_K", "Q8_0"]
-
-
-def _parse_quant_bits(model_name: str) -> Optional[int]:
-    """Extract quantization bit depth from a model filename or identifier."""
-    pattern = re.compile(r"Q([0-9]+)_?K?|Q([0-9]+)\.")
-    for match in pattern.finditer(model_name):
-        bits = match.group(1) or match.group(2)
-        if bits:
-            try:
-                return int(bits)
-            except ValueError:
-                pass
-    return None
-
-
-def check_quantization_floor(
-    model_name: str,
-    quant_type: str = "gguf",
-    awq_group_size: Optional[int] = None,
-) -> tuple[bool, str]:
-    """
-    Check if a model meets the minimum quantization floor.
-
-    For GGUF: minimum Q5_K_M (5-bit)
-    For AWQ/EXL2: minimum 4-bit with group_size ≤ 128
-
-    Returns (passes, detail_str).
-    """
-    if quant_type in ("gguf", "llama"):
-        bits = _parse_quant_bits(model_name)
-        if bits is None:
-            return True, "quantization bit depth unknown (GGUF)"
-        if bits < MIN_QUANT_BITS_GGUF:
-            return False, (
-                f"model is {bits}-bit; Q5_K_M (5-bit) is the minimum floor for GGUF. "
-                f"Models below Q5_K_M produce syntax errors in code generation."
-            )
-        detail = f"GGUF {bits}-bit (meets Q5_K_M floor)"
-        return True, detail
-
-    elif quant_type in ("awq", "exl2", "gptq"):
-        bits = _parse_quant_bits(model_name)
-        if bits is None:
-            return True, "quantization bit depth unknown (AWQ/EXL2)"
-        if bits < MIN_QUANT_BITS_AWQ:
-            return False, (
-                f"model is {bits}-bit; AWQ/EXL2 requires 4-bit minimum. "
-                f"Use AWQ 4-bit with group_size ≤ {MIN_AWQ_GROUP_SIZE}."
-            )
-        if awq_group_size is not None and awq_group_size > MIN_AWQ_GROUP_SIZE:
-            return False, (
-                f"AWQ group_size={awq_group_size} exceeds maximum {MIN_AWQ_GROUP_SIZE}. "
-                f"For reliable code generation, use group_size ≤ {MIN_AWQ_GROUP_SIZE}."
-            )
-        detail = f"AWQ/EXL2 {bits}-bit group_size={awq_group_size or 'default'} (meets floor)"
-        return True, detail
-
-    return True, "quantization type unrecognized, skipping check"
-
-
 class InferenceProvider(str, Enum):
     """Supported inference providers"""
 
@@ -131,10 +64,6 @@ class InferenceConfig:
     # vLLM specific
     vllm_url: Optional[str] = None
 
-    # Quantization metadata (used for floor validation)
-    model_quant_type: str = "gguf"
-    awq_group_size: Optional[int] = None
-
     # Performance tuning
     max_tokens: int = 4096
     temperature: float = 0.1
@@ -149,14 +78,6 @@ class InferenceConfig:
     warmup_requests: int = 1
     keep_alive: int = 300  # seconds
 
-    def validate_quantization(self) -> tuple[bool, str]:
-        """Validate that the configured model meets the quantization floor."""
-        return check_quantization_floor(
-            self.model_name,
-            quant_type=self.model_quant_type,
-            awq_group_size=self.awq_group_size,
-        )
-
 
 @dataclass
 class InferenceResult:
@@ -221,8 +142,6 @@ def _load_config_from_env(self) -> InferenceConfig:
             runpod_api_key=os.getenv("RUNPOD_API_KEY"),
             sglang_url=os.getenv("SGLANG_URL"),
             vllm_url=os.getenv("VLLM_URL"),
-            model_quant_type=os.getenv("MODEL_QUANT_TYPE", "gguf").lower(),
-            awq_group_size=int(os.getenv("AWQ_GROUP_SIZE", "128")),
             max_tokens=int(os.getenv("MAX_TOKENS", "4096")),
             temperature=float(os.getenv("LLM_TEMPERATURE", "0.1")),
             timeout=int(os.getenv("INFERENCE_TIMEOUT", "120")),
@@ -231,14 +150,6 @@ def _load_config_from_env(self) -> InferenceConfig:
 
     def _initialize_client(self):
         """Initialize the appropriate HTTP client based on provider"""
-        passes, detail = self.config.validate_quantization()
-        if not passes:
-            logger.warning(
-                f"QUANTIZATION FLOOR WARNING for model '{self.config.model_name}': {detail}"
-            )
-        else:
-            logger.info(f"Quantization check for '{self.config.model_name}': {detail}")
-
         if self.config.endpoint_url:
             try:
                 from openai import OpenAI

ai_engine/mmsd/TRAINING_REPORT.md

@@ -145,74 +145,7 @@ python3 ai_engine/mmsd/train_portkit_coder.py
 
 ---
 
-## 4. Training Recipe (Catastrophic Forgetting Mitigation)
-
-Fine-tuning exclusively on MMSD domain-specific pairs risks **catastrophic forgetting**: the model overwrites general Java/JS knowledge with Minecraft-specific patterns. The fix is a **general programming data mix** (12% of training tokens).
-
-### Why 12%?
-
-- At `r=64` (QLoRA rank), many weights are updated → high risk of forgetting
-- 5–15% is the standard range cited in fine-tuning literature
-- 12% preserves general reasoning while allowing MMSD specialization
-
-### Mixing Procedure
-
-```python
-from datasets import load_dataset, concatenate_datasets
-
-# 1. Load MMSD (validated_pairs.jsonl)
-mmsd = load_dataset("json", data_files="validated_pairs.jsonl")["train"]  # 1,400 pairs
-
-# 2. Load general code dataset — filter to Java + JavaScript
-general = load_dataset("m-a-p/CodeFeedback-Filtered-Instruction", split="train")
-general_java_js = general.filter(lambda x: x["lang"] in ["java", "javascript"])
-
-# 3. Sample ~200 general pairs, shuffle deterministically
-general_sample = general_java_js.shuffle(seed=42).select(range(200))
-
-# 4. Format general examples to match Stage A prompt template
-# (system prompt + user instruction + assistant code response)
-
-# 5. Mix to achieve ~12% general / ~88% MMSD by token count
-mixed = concatenate_datasets([mmsd_formatted, general_formatted])
-mixed_token_ratio = min(general_tokens / (mmsd_tokens + general_tokens), 0.12)
-
-# 6. Shuffle and split 90/10
-mixed = mixed.shuffle(seed=42)
-```
-
-### General Code Dataset
-
-| Property | Value |
-|----------|-------|
-| Dataset | `m-a-p/CodeFeedback-Filtered-Instruction` |
-| Languages | Java, JavaScript |
-| Sample size | ~200 instruction pairs |
-| Prompt template | General code assistant (not PortKit-specific) |
-| Caching | `/tmp/portkit_general_code/general_code_sample.jsonl` |
-
-### Expected Effects
-
-| Metric | Without Mix | With Mix (12%) |
-|--------|------------|----------------|
-| General Java/JS tasks | Degraded | ≤ 2% regression |
-| MMSD task quality | Baseline | Improved consistency |
-| Edge cases (abstract classes, generics, lambdas) | May degrade | Better handling |
-
-### Verification
-
-To evaluate the effect of the mix on general code tasks:
-```bash
-python3 ai_engine/mmsd/evaluate.py \
-    --model alexchapin/portkit-coder-7b-merged \
-    --baseline Qwen/Qwen2.5-Coder-7B-Instruct \
-    --eval-data ai_engine/mmsd/data/processed/validated_pairs.jsonl \
-    --output evaluation_results.json
-```
-
----
-
-## 5. Evaluation
+## 4. Evaluation
 
 ### Evaluation Script
 ```bash
@@ -238,7 +171,7 @@ python3 ai_engine/mmsd/evaluate.py \
 
 ---
 
-## 6. Hugging Face Hub Repositories
+## 5. Hugging Face Hub Repositories
 
 | Repository | Description | URL |
 |------------|-------------|-----|
@@ -249,7 +182,7 @@ Both repos are set to **private** visibility.
 
 ---
 
-## 7. Pipeline Verification
+## 6. Pipeline Verification
 
 The training pipeline was verified end-to-end using `Qwen/Qwen2.5-Coder-0.5B` on CPU:
 

ai_engine/mmsd/premium_client.py

@@ -14,11 +14,12 @@
 """
 
 import os
+import json
 import re
 import time
 import logging
 from typing import Optional
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 import httpx